Dragonfly-Support Vector Machine for Regression Modeling of the Activity Coefficient at Infinite Dilution of Solutes in Imidazolium Ionic Liquids Using σ-Profile Descriptors,Journal of Chemical & Engineering Data

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Dragonfly-Support Vector Machine for Regression Modeling of the Activity Coefficient at Infinite Dilution of Solutes in Imidazolium Ionic Liquids Using σ-Profile Descriptors
Journal of Chemical & Engineering Data ( IF 2.0 ) Pub Date : 2020-05-28 , DOI: 10.1021/acs.jced.0c00168
Hania Benimam ₁ , Cherif Si Moussa _{1,

2} , Mohamed Hentabli _{1,

2,

3} , Salah Hanini ₁ , Maamar Laidi _{1,

2}

Affiliation

Ionic liquids (ILs) have shown remarkable potential for applications in separation, such as extractive distillation and liquid–liquid extraction. Crucial to these applications is the estimation of a significant property of the ILs which is the infinite dilution activity coefficient (IDAC) of different solutes in ILs. In this context, the present paper aims to model IDAC of 17 solutes in 44 imidazolium ILs using 2666 experimental data points gathered from the literature and based on support vector machine for the regression (SVMr) learning algorithm. Two models are developed, one based on SVMr and the other one based on dragonfly algorithm (DA) associated with SVMr. Both models consider the same set of predictive variables which are the temperature, the molecular weight of solute and solvent, and five conductor-like screening models for real solvents (COSMO-RS) σ-profile descriptors related to the solute and IL. The DA is applied for optimization of SVMr hyper-parameters. The results show the superiority of the DA-SVMr model demonstrated by its correlation coefficient (R) and root mean square error values of 0.996 and 0.170, respectively.

中文翻译：

蜻蜓支持向量机，使用σ轮廓描述子对咪唑鎓离子液体中的溶质无限稀释时的活度系数进行回归建模

离子液体（IL）在分离中显示出了巨大的潜力，例如萃取蒸馏和液-液萃取。对于这些应用而言，至关重要的是估算IL的重要特性，即IL中不同溶质的无限稀释活度系数（IDAC）。在这种情况下，本论文旨在使用文献中收集的2666个实验数据点并基于支持向量机进行回归（SVMr）学习算法，对44个咪唑类ILs中17种溶质的IDAC进行建模。开发了两种模型，一种基于SVMr，另一种基于与SVMr相关的蜻蜓算法（DA）。两种模型均考虑同一组预测变量，例如温度，溶质和溶剂的分子量，五个与溶质和IL有关的真实溶剂（COSMO-RS）σ轮廓描述子的类似导体的筛选模型。DA用于优化SVMr超参数。结果表明，DA-SVMr模型的相关系数（R）和均方根误差值分别为0.996和0.170。

更新日期：2020-05-28

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